# Installing the libraries with the specified version.
!pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user

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  WARNING: The scripts f2py, f2py3 and f2py3.10 are installed in '/root/.local/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.
google-colab 1.0.0 requires pandas==2.1.4, but you have pandas 1.5.3 which is incompatible.
mizani 0.11.4 requires pandas>=2.1.0, but you have pandas 1.5.3 which is incompatible.
pandas-stubs 2.1.4.231227 requires numpy>=1.26.0; python_version < "3.13", but you have numpy 1.25.2 which is incompatible.
plotnine 0.13.6 requires pandas<3.0.0,>=2.1.0, but you have pandas 1.5.3 which is incompatible.
xarray 2024.9.0 requires pandas>=2.1, but you have pandas 1.5.3 which is incompatible.

# Mounted at /content/drive for Google Colab to get required data from drive
from google.colab import drive
drive.mount("/content/drive", force_remount=True)

Mounted at /content/drive

# import libraries for data manipulation
import numpy as np
import pandas as pd

# import libraries for data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# import library for probability distribution and statistic data
import scipy.stats as stats

# statistical libraries for Python
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant

# prediction libraries for Python (Train/Test + Tree)
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from sklearn.metrics import (
    accuracy_score,
    recall_score,
    confusion_matrix,
    make_scorer,
    roc_auc_score,
    roc_curve,
)
# disable display column & row limits
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 250)

# ignore warnings
import warnings
warnings.filterwarnings("ignore")

# read the data from dataset using panda
df = pd.read_csv("/content/drive/MyDrive/ColabNotebooks/INNHotelsGroup.csv")
data = df.copy()

# view first five rows of data
df.head()

# view last five rows od data
df.tail()

# I get same random results every time
np.random.seed(1)
df.sample(n=50)

# view the size of the data set
df.shape

(36275, 19)

# view the data types of a data set
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB

# check for duplicate values
df[df.duplicated()].count()

# checking for null values data is complete
df.isnull().sum()

# To drop booking_id
data = data.drop('Booking_ID', axis=1)

# view what are the values in object data type
cat_columns =['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status']
for i in cat_columns:
    print(data[i].value_counts())
    print("*" * 50)

type_of_meal_plan
Meal Plan 1     27835
Not Selected     5130
Meal Plan 2      3305
Meal Plan 3         5
Name: count, dtype: int64
**************************************************
room_type_reserved
Room_Type 1    28130
Room_Type 4     6057
Room_Type 6      966
Room_Type 2      692
Room_Type 5      265
Room_Type 7      158
Room_Type 3        7
Name: count, dtype: int64
**************************************************
market_segment_type
Online           23214
Offline          10528
Corporate         2017
Complementary      391
Aviation           125
Name: count, dtype: int64
**************************************************
booking_status
Not_Canceled    24390
Canceled        11885
Name: count, dtype: int64
**************************************************

data.describe()

data.describe(include=['object'])

# To create labeled barplots

def labeled_barplot(data, feature, perc=False, n=None):

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

# To plot a boxplot and a histogram along the same scale.

def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

labeled_barplot(data=data, feature="arrival_month", perc=True, n=None)

labeled_barplot(data, 'booking_status', perc=True, n=None)

labeled_barplot(data, 'market_segment_type', perc=True, n=None)

labeled_barplot(data, 'no_of_adults', perc=True)

labeled_barplot(df, 'no_of_children', perc=True)

labeled_barplot(df, 'no_of_weekend_nights', perc=True)

labeled_barplot(df,'no_of_week_nights', perc=True)

labeled_barplot(df, 'required_car_parking_space', perc=True)

labeled_barplot(df,'room_type_reserved', perc=True)

labeled_barplot(df,'type_of_meal_plan',perc=True)

labeled_barplot(df,'lead_time',perc=True)

histogram_boxplot(df,'no_of_previous_cancellations')

histogram_boxplot(df,'no_of_previous_bookings_not_canceled')

histogram_boxplot(data=df, feature="lead_time")

# To find Hotel free rooms
data[data['avg_price_per_room']==0]

df.loc[df['avg_price_per_room']==0, 'market_segment_type'].value_counts()

sns.boxplot(data=df, y='avg_price_per_room' , x='market_segment_type')

<Axes: xlabel='market_segment_type', ylabel='avg_price_per_room'>

sns.catplot(data=df ,y='no_of_special_requests', hue='booking_status', kind='count')

<seaborn.axisgrid.FacetGrid at 0x798549905510>

plt.figure(figsize=(20,10))
sns.heatmap(df.select_dtypes(include=np.number).corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<Axes: >

# hoe does lead time effect cancellation
sns.catplot(data=df, x='lead_time', hue='booking_status', kind='count')
plt.xticks(rotation=45)
plt.show()

# To find booking status count
sns.catplot(data=df, x='no_of_weekend_nights', hue='booking_status', kind='count')
plt.show()

sns.catplot(data=df, x='no_of_weekend_nights', hue='booking_status', kind='count')
plt.show()

# To find some of the months are busier than others
plt.figure(figsize=(10,5))
sns.lineplot(data=df, x='arrival_month', y='avg_price_per_room')
plt.show()

# new column for length to stay
data['length_stay'] = data['no_of_weekend_nights'] + data['no_of_week_nights']

sns.pairplot(df[['no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space','lead_time', 'avg_price_per_room', 'no_of_special_requests',
                 'type_of_meal_plan','room_type_reserved','market_segment_type','booking_status', ]]);

data.loc[data['booking_status']=='Not_Canceled', 'booking_status'] = False
data.loc[data['booking_status']=='Canceled', 'booking_status'] = True

# drop colum beacuse they were either time, or not helpful

numeric_columns = data.select_dtypes(include=np.number).columns.tolist()
numeric_columns.remove("arrival_year")

plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    data.boxplot()

plt.xticks(rotation=90)
plt.show()

#  Avg price room
Quartiles = np.quantile(data['avg_price_per_room'][data['avg_price_per_room'].notnull()], [.25,.75])
power_4iqr = 4 * (Quartiles[1] - Quartiles[0])
print(f'Q1 = {Quartiles[0]}, Q3 = {Quartiles[1]}, 4*IQR =  {power_4iqr}')
outlier_powers = data.loc[np.abs(data['avg_price_per_room'] - data['avg_price_per_room'].median()) > power_4iqr, 'avg_price_per_room']
outlier_powers.shape

Q1 = 80.3, Q3 = 120.0, 4*IQR =  158.8

(49,)

# creating a list of columns
dist_cols = [
item for item in data.select_dtypes(include=np.number).columns
 ]

plt.figure(figsize=(15, 45))
 # looping the list and ploting histograns
for i in range(len(dist_cols)):
     plt.subplot(12,3,i+1)
     plt.hist(data[dist_cols[i]],bins=50)
     plt.tight_layout()
     plt.title(dist_cols[i], fontsize=15)

plt.show()

data2 = data.copy()
# removing because they are close to normal
dist_cols.remove('no_of_week_nights')
dist_cols.remove('no_of_adults')
dist_cols.remove('length_stay')
dist_cols.remove('avg_price_per_room')
# removing becasue they are boolean or time related.
dist_cols.remove('arrival_year')
dist_cols.remove('required_car_parking_space')
dist_cols.remove('arrival_date')
dist_cols.remove('arrival_month')
dist_cols.remove('repeated_guest')
# removing lead_time
dist_cols.remove('lead_time')

# using log transforms on columns
for col in dist_cols:
    data2[col + "_log"] = np.log1p(data2[col] +1)

# dropping the original columns
data2.drop(dist_cols, axis=1, inplace=True)
data2.head()

# viewing the distributions after the log transformation
dist_cols = [
    item for item in data2.select_dtypes(include=np.number).columns
]

#plot  histogram of all numeric columns

plt.figure(figsize=(15, 45))
for i in range(len(dist_cols)):
    plt.subplot(12, 3, i+1)
    plt.hist(data2[dist_cols[i]],bins=50)
    sns.histplot(data=data2, x=dist_cols[i], kde=True)
    plt.tight_layout()
    plt.title(dist_cols[i], fontsize=15)

plt.show()

# catergorical variables
dummy_data = pd.get_dummies (
    data2,
    columns = [
        'type_of_meal_plan',
        'room_type_reserved',
        'market_segment_type',
    ],
    drop_first=True,
)
dummy_data.head()

dummy_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 29 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  int64  
 1   no_of_week_nights                         36275 non-null  int64  
 2   required_car_parking_space                36275 non-null  int64  
 3   lead_time                                 36275 non-null  int64  
 4   arrival_year                              36275 non-null  int64  
 5   arrival_month                             36275 non-null  int64  
 6   arrival_date                              36275 non-null  int64  
 7   repeated_guest                            36275 non-null  int64  
 8   avg_price_per_room                        36275 non-null  float64
 9   booking_status                            36275 non-null  object 
 10  length_stay                               36275 non-null  int64  
 11  no_of_children_log                        36275 non-null  float64
 12  no_of_weekend_nights_log                  36275 non-null  float64
 13  no_of_previous_cancellations_log          36275 non-null  float64
 14  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 15  no_of_special_requests_log                36275 non-null  float64
 16  type_of_meal_plan_Meal Plan 2             36275 non-null  bool   
 17  type_of_meal_plan_Meal Plan 3             36275 non-null  bool   
 18  type_of_meal_plan_Not Selected            36275 non-null  bool   
 19  room_type_reserved_Room_Type 2            36275 non-null  bool   
 20  room_type_reserved_Room_Type 3            36275 non-null  bool   
 21  room_type_reserved_Room_Type 4            36275 non-null  bool   
 22  room_type_reserved_Room_Type 5            36275 non-null  bool   
 23  room_type_reserved_Room_Type 6            36275 non-null  bool   
 24  room_type_reserved_Room_Type 7            36275 non-null  bool   
 25  market_segment_type_Complementary         36275 non-null  bool   
 26  market_segment_type_Corporate             36275 non-null  bool   
 27  market_segment_type_Offline               36275 non-null  bool   
 28  market_segment_type_Online                36275 non-null  bool   
dtypes: bool(13), float64(6), int64(9), object(1)
memory usage: 4.9+ MB

dummied_cut = pd.cut(dummy_data['lead_time'], 5, labels=['lat_min','short','med','long','advanced'])
dummied_cut.head(10)

data3 = pd.merge(dummy_data, dummied_cut, left_index=True, right_index=True)
data3.head().T

# dropping time variables.
data3_5 = data3.drop(['lead_time_x','arrival_date', 'arrival_year'], axis=1)

data4 = pd.get_dummies (
    data3_5,
    columns = [
        'lead_time_y',
    ],
    drop_first=True,
)
data4.head().T

data4 = data4.astype(float)

data4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   no_of_adults                              36275 non-null  float64
 1   no_of_week_nights                         36275 non-null  float64
 2   required_car_parking_space                36275 non-null  float64
 3   arrival_month                             36275 non-null  float64
 4   repeated_guest                            36275 non-null  float64
 5   avg_price_per_room                        36275 non-null  float64
 6   booking_status                            36275 non-null  float64
 7   length_stay                               36275 non-null  float64
 8   no_of_children_log                        36275 non-null  float64
 9   no_of_weekend_nights_log                  36275 non-null  float64
 10  no_of_previous_cancellations_log          36275 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  36275 non-null  float64
 12  no_of_special_requests_log                36275 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             36275 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             36275 non-null  float64
 15  type_of_meal_plan_Not Selected            36275 non-null  float64
 16  room_type_reserved_Room_Type 2            36275 non-null  float64
 17  room_type_reserved_Room_Type 3            36275 non-null  float64
 18  room_type_reserved_Room_Type 4            36275 non-null  float64
 19  room_type_reserved_Room_Type 5            36275 non-null  float64
 20  room_type_reserved_Room_Type 6            36275 non-null  float64
 21  room_type_reserved_Room_Type 7            36275 non-null  float64
 22  market_segment_type_Complementary         36275 non-null  float64
 23  market_segment_type_Corporate             36275 non-null  float64
 24  market_segment_type_Offline               36275 non-null  float64
 25  market_segment_type_Online                36275 non-null  float64
 26  lead_time_y_short                         36275 non-null  float64
 27  lead_time_y_med                           36275 non-null  float64
 28  lead_time_y_long                          36275 non-null  float64
 29  lead_time_y_advanced                      36275 non-null  float64
dtypes: float64(30)
memory usage: 8.3 MB

# the train test data first.
X = data4.drop("booking_status" , axis=1)
y = data4.pop("booking_status")

# adding a contstant to X variable
X = add_constant(X)

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

print("Number of rows in train data =", x_train.shape[0])
print("Number of rows in test data =", x_test.shape[0])

Number of rows in train data = 29020
Number of rows in test data = 7255

x_train.info()

<class 'pandas.core.frame.DataFrame'>
Index: 29020 entries, 25629 to 15795
Data columns (total 30 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     29020 non-null  float64
 1   no_of_adults                              29020 non-null  float64
 2   no_of_week_nights                         29020 non-null  float64
 3   required_car_parking_space                29020 non-null  float64
 4   arrival_month                             29020 non-null  float64
 5   repeated_guest                            29020 non-null  float64
 6   avg_price_per_room                        29020 non-null  float64
 7   length_stay                               29020 non-null  float64
 8   no_of_children_log                        29020 non-null  float64
 9   no_of_weekend_nights_log                  29020 non-null  float64
 10  no_of_previous_cancellations_log          29020 non-null  float64
 11  no_of_previous_bookings_not_canceled_log  29020 non-null  float64
 12  no_of_special_requests_log                29020 non-null  float64
 13  type_of_meal_plan_Meal Plan 2             29020 non-null  float64
 14  type_of_meal_plan_Meal Plan 3             29020 non-null  float64
 15  type_of_meal_plan_Not Selected            29020 non-null  float64
 16  room_type_reserved_Room_Type 2            29020 non-null  float64
 17  room_type_reserved_Room_Type 3            29020 non-null  float64
 18  room_type_reserved_Room_Type 4            29020 non-null  float64
 19  room_type_reserved_Room_Type 5            29020 non-null  float64
 20  room_type_reserved_Room_Type 6            29020 non-null  float64
 21  room_type_reserved_Room_Type 7            29020 non-null  float64
 22  market_segment_type_Complementary         29020 non-null  float64
 23  market_segment_type_Corporate             29020 non-null  float64
 24  market_segment_type_Offline               29020 non-null  float64
 25  market_segment_type_Online                29020 non-null  float64
 26  lead_time_y_short                         29020 non-null  float64
 27  lead_time_y_med                           29020 non-null  float64
 28  lead_time_y_long                          29020 non-null  float64
 29  lead_time_y_advanced                      29020 non-null  float64
dtypes: float64(30)
memory usage: 6.9 MB

plt.figure(figsize=(20,10))
sns.heatmap(
data4.corr(), annot=True, vmin=-1, vmax=1, fmt='.2f')

<Axes: >

sns.pairplot(data4[['no_of_adults',
 'required_car_parking_space',
 'arrival_month',
 'repeated_guest',
 'avg_price_per_room',
 'length_stay',
 'no_of_children_log',
 'no_of_previous_cancellations_log',
 'no_of_previous_bookings_not_canceled_log',
 'no_of_special_requests_log',
 'market_segment_type_Complementary',
 'market_segment_type_Corporate',
 'market_segment_type_Offline',
 'market_segment_type_Online',
 'lead_time_y_short',
 'lead_time_y_med',
 'lead_time_y_long',
 'lead_time_y_advanced']]);

# let's check the VIF of the predictor
vif_series = pd.Series(
    [variance_inflation_factor(x_train.values, i) for i in range(x_train.shape[1])],
    index=x_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                       834.261073
no_of_adults                                  1.341576
no_of_week_nights                           193.304696
required_car_parking_space                    1.041068
arrival_month                                 1.051504
repeated_guest                                2.847785
avg_price_per_room                            1.934505
length_stay                                 287.553100
no_of_children_log                            1.910321
no_of_weekend_nights_log                     68.425417
no_of_previous_cancellations_log              1.535978
no_of_previous_bookings_not_canceled_log      2.993684
no_of_special_requests_log                    1.265732
type_of_meal_plan_Meal Plan 2                 1.212041
type_of_meal_plan_Meal Plan 3                 1.014148
type_of_meal_plan_Not Selected                1.240739
room_type_reserved_Room_Type 2                1.087549
room_type_reserved_Room_Type 3                1.003615
room_type_reserved_Room_Type 4                1.359499
room_type_reserved_Room_Type 5                1.032541
room_type_reserved_Room_Type 6                1.893696
room_type_reserved_Room_Type 7                1.103061
market_segment_type_Complementary             4.540103
market_segment_type_Corporate                17.397158
market_segment_type_Offline                  65.070216
market_segment_type_Online                   72.414962
lead_time_y_short                             1.119323
lead_time_y_med                               1.106042
lead_time_y_long                              1.144803
lead_time_y_advanced                          1.046140
dtype: float64

# dropping the number of weekend & week night because  i have combained them into one & market segments because they all have large multi value
x_train1 = x_train.drop(['no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary'],
                       axis=1)

logit = sm.Logit(y_train, x_train1.astype(float)).fit()
logit.summary()

Optimization terminated successfully.
         Current function value: 0.460614
         Iterations 10

vif_series = pd.Series(
    [variance_inflation_factor(x_train1.values, i) for i in range(x_train1.shape[1])],
    index=x_train1.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                       181.913628
no_of_adults                                  1.276251
required_car_parking_space                    1.036220
arrival_month                                 1.045303
repeated_guest                                2.704601
avg_price_per_room                            1.577441
length_stay                                   1.078990
no_of_children_log                            1.901144
no_of_previous_cancellations_log              1.514158
no_of_previous_bookings_not_canceled_log      2.944955
no_of_special_requests_log                    1.135144
type_of_meal_plan_Meal Plan 2                 1.129908
type_of_meal_plan_Meal Plan 3                 1.009422
type_of_meal_plan_Not Selected                1.107915
room_type_reserved_Room_Type 2                1.075407
room_type_reserved_Room_Type 3                1.000657
room_type_reserved_Room_Type 4                1.313223
room_type_reserved_Room_Type 5                1.015376
room_type_reserved_Room_Type 6                1.864319
room_type_reserved_Room_Type 7                1.061829
lead_time_y_short                             1.104850
lead_time_y_med                               1.091866
lead_time_y_long                              1.116167
lead_time_y_advanced                          1.042915
dtype: float64

# test performance
pred_train = logit.predict(x_train1) > 0.5
pred_train = np.round(pred_train)

X_train2 = x_train1.drop(['room_type_reserved_Room_Type 3'], axis=1)
X_train2.info()

<class 'pandas.core.frame.DataFrame'>
Index: 29020 entries, 25629 to 15795
Data columns (total 23 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   const                                     29020 non-null  float64
 1   no_of_adults                              29020 non-null  float64
 2   required_car_parking_space                29020 non-null  float64
 3   arrival_month                             29020 non-null  float64
 4   repeated_guest                            29020 non-null  float64
 5   avg_price_per_room                        29020 non-null  float64
 6   length_stay                               29020 non-null  float64
 7   no_of_children_log                        29020 non-null  float64
 8   no_of_previous_cancellations_log          29020 non-null  float64
 9   no_of_previous_bookings_not_canceled_log  29020 non-null  float64
 10  no_of_special_requests_log                29020 non-null  float64
 11  type_of_meal_plan_Meal Plan 2             29020 non-null  float64
 12  type_of_meal_plan_Meal Plan 3             29020 non-null  float64
 13  type_of_meal_plan_Not Selected            29020 non-null  float64
 14  room_type_reserved_Room_Type 2            29020 non-null  float64
 15  room_type_reserved_Room_Type 4            29020 non-null  float64
 16  room_type_reserved_Room_Type 5            29020 non-null  float64
 17  room_type_reserved_Room_Type 6            29020 non-null  float64
 18  room_type_reserved_Room_Type 7            29020 non-null  float64
 19  lead_time_y_short                         29020 non-null  float64
 20  lead_time_y_med                           29020 non-null  float64
 21  lead_time_y_long                          29020 non-null  float64
 22  lead_time_y_advanced                      29020 non-null  float64
dtypes: float64(23)
memory usage: 5.3 MB

logit2 = sm.Logit(y_train, X_train2.astype(float)).fit()
logit2.summary()

Optimization terminated successfully.
         Current function value: 0.460616
         Iterations 10

x_train3 = X_train2.drop(['no_of_previous_bookings_not_canceled_log'], axis=1)

logit3 = sm.Logit(y_train, x_train3.astype(float)).fit()
logit3.summary()

Optimization terminated successfully.
         Current function value: 0.460635
         Iterations 9

vif_series = pd.Series(
    [variance_inflation_factor(x_train3.values, i) for i in range(x_train3.shape[1])],
    index=x_train3.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                               175.817619
no_of_adults                          1.273740
required_car_parking_space            1.035127
arrival_month                         1.044396
repeated_guest                        1.465019
avg_price_per_room                    1.574018
length_stay                           1.078836
no_of_children_log                    1.901014
no_of_previous_cancellations_log      1.343447
no_of_special_requests_log            1.130055
type_of_meal_plan_Meal Plan 2         1.129777
type_of_meal_plan_Meal Plan 3         1.009408
type_of_meal_plan_Not Selected        1.107794
room_type_reserved_Room_Type 2        1.075386
room_type_reserved_Room_Type 4        1.312711
room_type_reserved_Room_Type 5        1.014418
room_type_reserved_Room_Type 6        1.863570
room_type_reserved_Room_Type 7        1.061215
lead_time_y_short                     1.104778
lead_time_y_med                       1.091840
lead_time_y_long                      1.116088
lead_time_y_advanced                  1.042863
dtype: float64

x_train4 = x_train3.drop(['room_type_reserved_Room_Type 2'], axis=1)

logit4 = sm.Logit(y_train, x_train4.astype(float)).fit()
logit4.summary()

Optimization terminated successfully.
         Current function value: 0.460647
         Iterations 9

x_train5 = x_train4.drop(['room_type_reserved_Room_Type 4'], axis=1)

logit5 = sm.Logit(y_train, x_train5.astype(float)).fit()
logit5.summary()

Optimization terminated successfully.
         Current function value: 0.460669
         Iterations 9

odds_ratio = np.exp(logit5.params)
pd.DataFrame(odds_ratio, x_train5.columns, columns=["Odds Ratio"]).T

# finding the percentage change
perc_change_odds = (np.exp(logit5.params) - 1) * 100

# adding the change_odds% to a dataframe
pd.DataFrame(perc_change_odds, x_train5.columns, columns=["change_odds%"]).T

# fitting the model on training set
logit = sm.Logit(y_train, x_train5.astype(float))
lg3 = logit.fit()

pred_train4 = logit5.predict(x_train5)
pred_train4 = np.round(pred_train4)

Optimization terminated successfully.
         Current function value: 0.460669
         Iterations 9

cm = confusion_matrix(y_train, pred_train4)
plt.figure(figsize=(7, 5))
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()

print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))

Accuracy on training set :  0.7816678152997932

logit_roc_auc_train = roc_auc_score(y_train, logit5.predict(x_train5))
fpr, tpr, thresholds = roc_curve(y_train, logit5.predict(x_train5))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

X_test1 = x_test.drop([ 'no_of_weekend_nights_log',
                         'no_of_week_nights',
                         'market_segment_type_Online',
                         'market_segment_type_Offline',
                         'market_segment_type_Corporate',
                        'market_segment_type_Complementary',
                       'room_type_reserved_Room_Type 3',
                       'room_type_reserved_Room_Type 4',
                       'no_of_previous_bookings_not_canceled_log',
                       'room_type_reserved_Room_Type 2'

                     ], axis=1)

pred_test = logit5.predict(X_test1) > 0.5
pred_test = np.round(pred_test)
print("Accuracy on training set : ", accuracy_score(y_train, pred_train4))
print("Accuracy on test set : ", accuracy_score(y_test, pred_test))

Accuracy on training set :  0.7816678152997932
Accuracy on test set :  0.7789110957960027

tree_data = dummy_data.astype(float)

tree_data = tree_data.drop(['arrival_date','arrival_year','no_of_week_nights',
'no_of_weekend_nights_log'  ], axis=1)

x = tree_data.drop("booking_status" , axis=1)
y = tree_data.pop("booking_status")

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.30, random_state=1)

# building a decision tree using the dtclassifier function
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=1)
dTree.fit(x_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)

# scoring the accurancy on train & test data
print("Accuracy on training set : ", dTree.score(x_train, y_train))
print("Accuracy on test set : ", dTree.score(x_test, y_test))

Accuracy on training set :  0.9924385633270322
Accuracy on test set :  0.8585867867315997

# checking the positive outcomes
y.sum(axis = 0)

11885.0

# function to create confusion matrix
def make_confusion_matrix(model,y_actual,labels=[1,0]):
    y_predict =  model.predict(x_test)
    cm =metrics.confusion_matrix(y_actual, y_predict, labels=[0,1])
    df_cm = pd.DataFrame(cm, index = [i for i in["Actucal - No" , "Actual - Yes"]],
                  columns = [i for i in ["Predicted - No" , "Predicted - Yes"]])
    group_counts=["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
                zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('predicated label')

## function to calculate recall score
def get_recall_score(model):
    pred_train = model.predict(x_train)
    pred_test = model.predict(x_test)
    print("Recall on training set : ",metrics. recall_score(y_train, pred_train))
    print("Recall on test set : ",metrics. recall_score(y_test, pred_test))

# another confusion matrix
dtree = DecisionTreeClassifier()
dtree.fit(x_train, y_train)
make_confusion_matrix(dtree,y_test)

# check the recall on the train to test
get_recall_score(dtree)

Recall on training set :  0.9817051297381323
Recall on test set :  0.7924474730266894

the_features = list(x.columns)
print(the_features)

['no_of_adults', 'required_car_parking_space', 'lead_time', 'arrival_month', 'repeated_guest', 'avg_price_per_room', 'length_stay', 'no_of_children_log', 'no_of_previous_cancellations_log', 'no_of_previous_bookings_not_canceled_log', 'no_of_special_requests_log', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']

plt.figure(figsize=(20,30))
tree.plot_tree(dtree,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

print(tree.export_text(dtree,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.90
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |--- avg_price_per_room <= 201.50
|   |   |   |   |   |   |--- lead_time <= 74.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 151.59
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  151.59
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [169.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  50.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |--- lead_time >  74.50
|   |   |   |   |   |   |   |--- lead_time <= 78.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.47
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [26.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.47
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  78.50
|   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [110.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  86.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  63.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.75
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 73.53
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  73.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |--- avg_price_per_room >  201.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |--- length_stay <= 14.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  75.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.42
|   |   |   |   |   |   |   |   |   |--- weights: [34.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.42
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.42
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- length_stay >  14.50
|   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 137.80
|   |   |   |   |   |   |   |   |--- length_stay <= 10.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 43.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  10.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  137.80
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- avg_price_per_room <= 93.58
|   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [50.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  112.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- avg_price_per_room >  93.58
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 59.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 101.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  101.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 101.12
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  101.12
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 47.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 177.83
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [45.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  177.83
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.86
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.86
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |--- weights: [141.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.12
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 124.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  124.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.12
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [65.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.75
|   |   |   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  138.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.75
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 223.26
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.41
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.41
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [60.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  223.26
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 13.50
|   |   |   |   |--- avg_price_per_room <= 202.67
|   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.40
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.35
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 182.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  182.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [84.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [42.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  119.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 160.17
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [41.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  160.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.67
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 32.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |--- lead_time >  13.50
|   |   |   |   |--- avg_price_per_room <= 105.27
|   |   |   |   |   |--- avg_price_per_room <= 60.07
|   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |--- lead_time <= 51.50
|   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 21.67
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  21.67
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  49.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  51.50
|   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |--- length_stay <= 8.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 59.43
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  59.43
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  8.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- avg_price_per_room >  60.07
|   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [54.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 57.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  57.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 35.00] class: 1.0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  105.27
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 195.30
|   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  38.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  135.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |--- avg_price_per_room >  195.30
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 92.00] class: 1.0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.06
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 147.75
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  147.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.06
|   |   |   |   |   |   |   |   |   |--- length_stay <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 80.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- length_stay <= 11.00
|   |   |   |   |   |   |   |--- weights: [39.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  11.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |--- no_of_special_requests_log >  0.90
|   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |--- length_stay <= 15.00
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [848.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.90
|   |   |   |   |   |   |   |   |   |   |--- weights: [43.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_children_log >  0.90
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |--- length_stay >  15.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |--- lead_time <= 104.50
|   |   |   |   |   |   |   |--- lead_time <= 103.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.90
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.90
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  104.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  141.75
|   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |--- lead_time <= 63.00
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  63.00
|   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- length_stay <= 14.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 219.86
|   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.64
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.64
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 158.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  158.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 113.02
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  113.02
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  219.86
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 223.58
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  223.58
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 237.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  237.25
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 244.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  244.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- length_stay >  14.00
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 123.60
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.76
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.76
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- weights: [95.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  123.60
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.05
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.05
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 94.48
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  94.48
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 127.62
|   |   |   |   |   |   |   |--- lead_time <= 43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 9.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [87.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [127.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 29.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.22
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.22
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  29.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  43.50
|   |   |   |   |   |   |   |   |--- length_stay <= 10.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.54
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- length_stay >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |--- avg_price_per_room >  127.62
|   |   |   |   |   |   |   |--- lead_time <= 142.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.62
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.62
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 139.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  139.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [49.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- lead_time >  142.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 142.65
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  142.65
|   |   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 216.45
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  216.45
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |--- weights: [180.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |--- no_of_special_requests_log >  1.24
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- length_stay <= 12.00
|   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- weights: [1689.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.05
|   |   |   |   |   |   |   |   |   |--- lead_time <= 48.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  48.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.85
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.05
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [221.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 31.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  31.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  159.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.95
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.33
|   |   |   |   |   |   |   |   |   |--- lead_time <= 80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  80.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  81.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.00
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  9.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 34.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.24
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.24
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  34.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 72.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  72.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |   |   |   |--- weights: [69.00, 0.00] class: 0.0
|   |   |   |   |--- length_stay >  12.00
|   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- avg_price_per_room <= 202.95
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 98.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  98.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.33
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  115.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- no_of_children_log <= 0.90
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  142.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_children_log >  0.90
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.27
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.27
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 103.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  103.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 90.42
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 107.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.52
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.52
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  107.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 101.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  101.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- avg_price_per_room >  90.42
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 153.15
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.60
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  153.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  100.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |--- avg_price_per_room >  202.95
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1.0
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- lead_time <= 165.50
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [61.00, 6.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  165.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.35
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.21
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  231.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 402.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 2.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  402.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.33
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- avg_price_per_room <= 84.58
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 166.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  166.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 229.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.34
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  229.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 27.07
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  27.07
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.83
|   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.83
|   |   |   |   |   |   |   |   |   |--- lead_time <= 292.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  292.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 23.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [37.00, 0.00] class: 0.0
|   |   |   |   |   |--- avg_price_per_room >  84.58
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 316.00
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  316.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  338.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 5.00] class: 1.0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0.0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- lead_time <= 285.50
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- lead_time >  285.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- weights: [0.00, 525.00] class: 1.0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |   |--- lead_time <= 204.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  204.00
|   |   |   |   |   |   |   |   |--- lead_time <= 214.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  214.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 275.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  275.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 80.51
|   |   |   |   |   |   |   |   |--- weights: [0.00, 41.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  80.51
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.43
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.43
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- lead_time <= 152.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.81
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.81
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  152.50
|   |   |   |   |   |   |   |   |--- lead_time <= 156.50
|   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  156.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 87.12
|   |   |   |   |   |   |   |   |--- lead_time <= 158.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0
|   |   |   |   |   |   |   |   |--- lead_time >  158.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  87.12
|   |   |   |   |   |   |   |   |--- length_stay <= 2.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.62
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.62
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- length_stay >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 93.44
|   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [52.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  93.44
|   |   |   |   |   |   |   |   |--- lead_time <= 178.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.67
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 169.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  169.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.67
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- lead_time >  178.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 179.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  179.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 1.00] class: 0.0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- length_stay <= 3.50
|   |   |   |   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |   |   |   |--- lead_time <= 187.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.30
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.30
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 20.00] class: 1.0
|   |   |   |   |   |   |   |--- lead_time >  187.50
|   |   |   |   |   |   |   |   |--- lead_time <= 304.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.90
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  237.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.90
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |--- lead_time >  304.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1.0
|   |   |   |   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0.0
|   |   |   |   |   |--- length_stay >  3.50
|   |   |   |   |   |   |--- length_stay <= 13.50
|   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 247.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  247.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.32
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 292.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  292.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- length_stay >  13.50
|   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1.0
|   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |--- lead_time <= 368.00
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- length_stay <= 7.50
|   |   |   |   |   |   |   |   |--- lead_time <= 331.00
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |   |   |--- weights: [137.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |   |   |--- length_stay <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |   |   |--- length_stay >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |--- lead_time >  331.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0.0
|   |   |   |   |   |   |   |   |   |--- lead_time >  336.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- length_stay >  7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.74
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.74
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- lead_time <= 196.00
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |--- lead_time >  196.00
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1.0
|   |   |   |   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |--- weights: [6.00, 2.00] class: 0.0
|   |   |   |   |--- lead_time >  368.00
|   |   |   |   |   |--- lead_time <= 381.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |--- lead_time >  381.50
|   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests_log <= 1.50
|   |   |   |   |--- weights: [0.00, 2108.00] class: 1.0
|   |   |   |--- no_of_special_requests_log >  1.50
|   |   |   |   |--- weights: [31.00, 0.00] class: 0.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |   |--- weights: [47.00, 0.00] class: 0.0
|   |   |   |--- no_of_special_requests_log >  0.90
|   |   |   |   |--- lead_time <= 289.50
|   |   |   |   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |   |   |   |--- avg_price_per_room <= 114.59
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  114.59
|   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1.0
|   |   |   |   |   |--- no_of_special_requests_log >  1.24
|   |   |   |   |   |   |--- avg_price_per_room <= 199.65
|   |   |   |   |   |   |   |--- avg_price_per_room <= 110.46
|   |   |   |   |   |   |   |   |--- length_stay <= 5.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0.0
|   |   |   |   |   |   |   |   |--- length_stay >  5.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |   |   |   |--- avg_price_per_room >  110.46
|   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0.0
|   |   |   |   |   |   |--- avg_price_per_room >  199.65
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1.0
|   |   |   |   |--- lead_time >  289.50
|   |   |   |   |   |--- weights: [0.00, 7.00] class: 1.0

# checking out what variable are being prioritized by the model.
print (pd.DataFrame(dtree.feature_importances_, columns = ["Imp"], index = x_train.columns).sort_values(by = 'Imp', ascending = False))

                                               Imp
lead_time                                 0.399009
avg_price_per_room                        0.204342
market_segment_type_Online                0.092752
arrival_month                             0.083932
length_stay                               0.074351
no_of_special_requests_log                0.068483
no_of_adults                              0.030179
type_of_meal_plan_Not Selected            0.010868
required_car_parking_space                0.007417
room_type_reserved_Room_Type 4            0.007339
no_of_children_log                        0.006595
type_of_meal_plan_Meal Plan 2             0.004375
market_segment_type_Offline               0.003333
room_type_reserved_Room_Type 2            0.002304
room_type_reserved_Room_Type 5            0.001582
market_segment_type_Corporate             0.001144
room_type_reserved_Room_Type 6            0.000674
repeated_guest                            0.000464
room_type_reserved_Room_Type 7            0.000398
no_of_previous_cancellations_log          0.000323
no_of_previous_bookings_not_canceled_log  0.000137
room_type_reserved_Room_Type 3            0.000000
market_segment_type_Complementary         0.000000
type_of_meal_plan_Meal Plan 3             0.000000

importances = dtree.feature_importances_
indices = np.argsort(importances)
plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='red', align='center')
plt.yticks(range(len(indices)),[the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

## pre prune  the model with max depth hyperparamater
dTree1 = DecisionTreeClassifier(criterion = 'gini', max_depth=3,random_state=1)
dTree1.fit(x_train, y_train)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)

# another confusion matrix
make_confusion_matrix(dTree1,y_test)

# the accuracy  on the pre pruned tree
print("Accuracy on training set : ", dTree1.score(x_train, y_train))
print("Accuracy on test set : ", dTree1.score(x_test, y_test))
# checking the recall with the get_recall_score userdefined function
get_recall_score(dTree1)

Accuracy on training set :  0.7844202898550725
Accuracy on test set :  0.7913259211614444
Recall on training set :  0.7315556618438359
Recall on test set :  0.7385008517887564

# Let's see the pree pruned tree
plt.figure(figsize=(15,10))
tree.plot_tree(dTree1,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

print(tree.export_text(dTree1,feature_names=the_features,show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests_log <= 0.90
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [4614.00, 781.00] class: 0.0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2504.00, 2768.00] class: 1.0
|   |--- no_of_special_requests_log >  0.90
|   |   |--- no_of_special_requests_log <= 1.24
|   |   |   |--- weights: [5624.00, 1055.00] class: 0.0
|   |   |--- no_of_special_requests_log >  1.24
|   |   |   |--- weights: [2919.00, 145.00] class: 0.0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests_log <= 0.90
|   |   |   |--- weights: [694.00, 1242.00] class: 1.0
|   |   |--- no_of_special_requests_log >  0.90
|   |   |   |--- weights: [586.00, 249.00] class: 0.0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- weights: [31.00, 2108.00] class: 1.0
|   |   |--- arrival_month >  11.50
|   |   |   |--- weights: [57.00, 15.00] class: 0.0

# Looking at the feature importances of this model
importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10,10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
## add from article
parameters = {'max_depth': np.arange(1,10),
              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
              'max_leaf_nodes' : [2, 3, 5, 10],
              'min_impurity_decrease': [0.001,0.01,0.1]
             }

# scoring function used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(x_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(x_train, y_train)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)

DecisionTreeClassifier(max_depth=3, max_leaf_nodes=5,
                       min_impurity_decrease=0.001, random_state=1)

# run the estimator in a confusion matrix
make_confusion_matrix(estimator,y_test)

# The accuracy on the estimator tree.
print("Accuracy on training set : ",estimator.score(x_train, y_train))
print("Accuracy on test set : ",estimator.score(x_test, y_test))
# Check the recall with the get_recall_score user defined function
get_recall_score(dTree1)

Accuracy on training set :  0.7694943289224953
Accuracy on test set :  0.7719378847744188
Recall on training set :  0.7315556618438359
Recall on test set :  0.7385008517887564

plt.figure(figsize=(15,10))

tree.plot_tree(estimator,feature_names=the_features,filled=True,fontsize=9,node_ids=True,class_names=True)
plt.show()

importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(x_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities

pd.DataFrame(path)

fig, ax = plt.subplots(figsize=(10,5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker='o', drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()

#Decisiion Tree calssifier for every alpha
clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(x_train, y_train)
    clfs.append(clf)
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
      clfs[-1].tree_.node_count, ccp_alphas[-1]))

Number of nodes in the last tree is: 1 with ccp_alpha: 0.0765778947737134

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1,figsize=(10,7))
ax[0].plot(ccp_alphas, node_counts, marker='o', drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker='o', drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()

train_scores = [clf.score(x_train, y_train) for clf in clfs]
test_scores = [clf.score(x_test, y_test) for clf in clfs]

fig, ax = plt.subplots(figsize=(10,5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()

index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print('Training accuracy of best model: ',best_model.score(x_train, y_train))
print('Test accuracy of best model: ',best_model.score(x_test, y_test))

DecisionTreeClassifier(ccp_alpha=9.904212140385933e-05, random_state=1)
Training accuracy of best model:  0.90544265910523
Test accuracy of best model:  0.8783423688321235

recall_train=[]
for clf in clfs:
    pred_train3=clf.predict(x_train)
    values_train=metrics.recall_score(y_train,pred_train3)
    recall_train.append(values_train)

recall_test=[]
for clf in clfs:
    pred_test3=clf.predict(x_test)
    values_test=metrics.recall_score(y_test,pred_test3)
    recall_test.append(values_test)

fig, ax = plt.subplots(figsize=(15,5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()

# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=2.450465588461807e-05, random_state=1)

#another confusion matrix
make_confusion_matrix(best_model,y_test)

# Recall on train and test
get_recall_score(best_model)

Recall on training set :  0.9790744947985173
Recall on test set :  0.7935831913685406

# showing what metrics this model used
importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [the_features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

comparison_frame = pd.DataFrame({'Model':['Initial decision tree model','Decision tree with restricted maximum depth','Decision treee with hyperparameter tuning',
                                         'Decision tree with post-pruning'], 'Train_Recall':[.981,.732,.732,.979], 'Test_Recall':[.792,.739,.739,.794]})
comparison_frame

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests
count	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000	36275.000000
mean	1.844962	0.105279	0.810724	2.204300	0.030986	85.232557	2017.820427	7.423653	15.596995	0.025637	0.023349	0.153411	103.423539	0.619655
std	0.518715	0.402648	0.870644	1.410905	0.173281	85.930817	0.383836	3.069894	8.740447	0.158053	0.368331	1.754171	35.089424	0.786236
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2017.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2.000000	0.000000	0.000000	1.000000	0.000000	17.000000	2018.000000	5.000000	8.000000	0.000000	0.000000	0.000000	80.300000	0.000000
50%	2.000000	0.000000	1.000000	2.000000	0.000000	57.000000	2018.000000	8.000000	16.000000	0.000000	0.000000	0.000000	99.450000	0.000000
75%	2.000000	0.000000	2.000000	3.000000	0.000000	126.000000	2018.000000	10.000000	23.000000	0.000000	0.000000	0.000000	120.000000	1.000000
max	4.000000	10.000000	7.000000	17.000000	1.000000	443.000000	2018.000000	12.000000	31.000000	1.000000	13.000000	58.000000	540.000000	5.000000

Dep. Variable:	booking_status	No. Observations:	29020
Model:	Logit	Df Residuals:	28996
Method:	MLE	Df Model:	23
Date:	Fri, 27 Sep 2024	Pseudo R-squ.:	0.2706
Time:	20:50:39	Log-Likelihood:	-13367.
converged:	True	LL-Null:	-18327.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-2.7560	0.435	-6.334	0.000	-3.609	-1.903
no_of_adults	0.2461	0.033	7.465	0.000	0.181	0.311
required_car_parking_space	-1.4865	0.129	-11.518	0.000	-1.740	-1.234
arrival_month	-0.0660	0.005	-12.259	0.000	-0.076	-0.055
repeated_guest	-2.5765	0.532	-4.842	0.000	-3.619	-1.534
avg_price_per_room	0.0228	0.001	36.055	0.000	0.022	0.024
length_stay	0.1161	0.009	13.436	0.000	0.099	0.133
no_of_children_log	0.8147	0.145	5.626	0.000	0.531	1.098
no_of_previous_cancellations_log	1.4853	0.497	2.988	0.003	0.511	2.460
no_of_previous_bookings_not_canceled_log	-0.4811	0.499	-0.965	0.335	-1.458	0.496
no_of_special_requests_log	-3.2166	0.068	-47.388	0.000	-3.350	-3.084
type_of_meal_plan_Meal Plan 2	-0.3154	0.053	-5.955	0.000	-0.419	-0.212
type_of_meal_plan_Meal Plan 3	1.7119	2.879	0.595	0.552	-3.931	7.355
type_of_meal_plan_Not Selected	0.8094	0.045	17.976	0.000	0.721	0.898
room_type_reserved_Room_Type 2	0.0980	0.116	0.847	0.397	-0.129	0.325
room_type_reserved_Room_Type 3	-0.4403	1.308	-0.337	0.736	-3.004	2.124
room_type_reserved_Room_Type 4	0.0543	0.047	1.159	0.246	-0.038	0.146
room_type_reserved_Room_Type 5	-0.8687	0.180	-4.833	0.000	-1.221	-0.516
room_type_reserved_Room_Type 6	-1.0992	0.129	-8.510	0.000	-1.352	-0.846
room_type_reserved_Room_Type 7	-1.7658	0.277	-6.373	0.000	-2.309	-1.223
lead_time_y_short	1.3149	0.036	36.224	0.000	1.244	1.386
lead_time_y_med	2.8013	0.054	52.064	0.000	2.696	2.907
lead_time_y_long	3.0378	0.073	41.688	0.000	2.895	3.181
lead_time_y_advanced	4.5385	0.235	19.286	0.000	4.077	5.000

Dep. Variable:	booking_status	No. Observations:	29020
Model:	Logit	Df Residuals:	28997
Method:	MLE	Df Model:	22
Date:	Fri, 27 Sep 2024	Pseudo R-squ.:	0.2706
Time:	20:50:57	Log-Likelihood:	-13367.
converged:	True	LL-Null:	-18327.
Covariance Type:	nonrobust	LLR p-value:	0.000

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-2.7560	0.435	-6.334	0.000	-3.609	-1.903
no_of_adults	0.2461	0.033	7.464	0.000	0.181	0.311
required_car_parking_space	-1.4865	0.129	-11.517	0.000	-1.739	-1.234
arrival_month	-0.0660	0.005	-12.262	0.000	-0.077	-0.055
repeated_guest	-2.5764	0.532	-4.842	0.000	-3.619	-1.534
avg_price_per_room	0.0228	0.001	36.056	0.000	0.022	0.024
length_stay	0.1161	0.009	13.438	0.000	0.099	0.133
no_of_children_log	0.8148	0.145	5.627	0.000	0.531	1.099
no_of_previous_cancellations_log	1.4853	0.497	2.988	0.003	0.511	2.459
no_of_previous_bookings_not_canceled_log	-0.4811	0.499	-0.965	0.335	-1.458	0.496
no_of_special_requests_log	-3.2165	0.068	-47.387	0.000	-3.350	-3.083
type_of_meal_plan_Meal Plan 2	-0.3153	0.053	-5.953	0.000	-0.419	-0.211
type_of_meal_plan_Meal Plan 3	1.7120	2.879	0.595	0.552	-3.931	7.355
type_of_meal_plan_Not Selected	0.8095	0.045	17.978	0.000	0.721	0.898
room_type_reserved_Room_Type 2	0.0981	0.116	0.847	0.397	-0.129	0.325
room_type_reserved_Room_Type 4	0.0544	0.047	1.161	0.246	-0.037	0.146
room_type_reserved_Room_Type 5	-0.8686	0.180	-4.832	0.000	-1.221	-0.516
room_type_reserved_Room_Type 6	-1.0992	0.129	-8.510	0.000	-1.352	-0.846
room_type_reserved_Room_Type 7	-1.7658	0.277	-6.373	0.000	-2.309	-1.223
lead_time_y_short	1.3147	0.036	36.223	0.000	1.244	1.386
lead_time_y_med	2.8013	0.054	52.065	0.000	2.696	2.907
lead_time_y_long	3.0379	0.073	41.689	0.000	2.895	3.181
lead_time_y_advanced	4.5385	0.235	19.286	0.000	4.077	5.000

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Data Overview¶

Exploratory Data Analysis (EDA)¶

Data Preprocessing¶

EDA¶

Checking Multicollinearity¶

Building a Logistic Regression model¶

Model performance evaluation¶

Final Model Summary¶

Building a Decision Tree model¶

Do we need to prune the tree?¶

Model Performance Comparison and Conclusions¶

Actionable Insights and Recommendations¶

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50	0	Canceled

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
36270	INN36271	3	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80	1	Not_Canceled
36271	INN36272	2	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95	2	Canceled
36272	INN36273	2	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39	2	Not_Canceled
36273	INN36274	2	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50	0	Canceled
36274	INN36275	2	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67	0	Not_Canceled

	Booking_ID	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
30392	INN30393	1	0	1	0	Not Selected	0	Room_Type 1	53	2018	9	11	Online	0	0	0	94.32	0	Not_Canceled
6685	INN06686	2	0	1	2	Meal Plan 1	0	Room_Type 1	63	2018	4	22	Online	0	0	0	105.30	1	Canceled
8369	INN08370	2	0	2	3	Meal Plan 1	0	Room_Type 4	55	2018	9	11	Online	0	0	0	106.24	0	Not_Canceled
2055	INN02056	2	0	0	2	Not Selected	0	Room_Type 1	53	2017	12	29	Online	0	0	0	81.00	1	Not_Canceled
10969	INN10970	1	0	2	4	Meal Plan 1	0	Room_Type 1	245	2018	7	6	Offline	0	0	0	110.00	0	Canceled
24881	INN24882	2	0	3	7	Meal Plan 1	0	Room_Type 2	231	2018	8	1	Online	0	0	0	81.82	2	Canceled
28658	INN28659	2	0	0	3	Meal Plan 2	0	Room_Type 1	71	2018	5	10	Offline	0	0	0	126.00	1	Not_Canceled
20853	INN20854	2	0	1	2	Meal Plan 1	0	Room_Type 1	66	2017	10	9	Offline	0	0	0	75.00	0	Canceled
8501	INN08502	2	0	0	3	Meal Plan 1	0	Room_Type 2	40	2018	1	14	Online	0	0	0	77.55	1	Not_Canceled
1942	INN01943	2	0	0	2	Meal Plan 1	0	Room_Type 1	63	2018	8	9	Online	0	0	0	144.90	2	Not_Canceled
15648	INN15649	2	0	2	4	Meal Plan 1	0	Room_Type 1	209	2018	7	2	Online	0	0	0	66.53	1	Not_Canceled
6116	INN06117	2	0	0	1	Meal Plan 1	0	Room_Type 1	9	2018	7	6	Online	0	0	0	139.00	1	Not_Canceled
7868	INN07869	2	0	2	4	Meal Plan 1	0	Room_Type 4	123	2018	5	22	Online	0	0	0	114.75	1	Canceled
24527	INN24528	2	0	0	3	Meal Plan 1	0	Room_Type 1	118	2018	6	28	Online	0	0	0	96.30	0	Canceled
24227	INN24228	2	0	1	0	Meal Plan 1	0	Room_Type 4	72	2018	10	9	Online	0	0	0	132.30	3	Not_Canceled
17216	INN17217	2	0	0	1	Meal Plan 1	0	Room_Type 1	1	2018	11	23	Online	0	0	0	120.00	0	Not_Canceled
31124	INN31125	1	0	0	1	Meal Plan 1	0	Room_Type 1	7	2017	8	28	Corporate	1	1	2	65.00	0	Not_Canceled
9101	INN09102	1	0	2	1	Meal Plan 1	0	Room_Type 1	12	2018	10	2	Online	0	0	0	6.00	0	Not_Canceled
9474	INN09475	2	0	0	2	Meal Plan 2	0	Room_Type 1	63	2017	9	4	Offline	0	0	0	116.00	0	Not_Canceled
12782	INN12783	2	0	0	3	Not Selected	0	Room_Type 1	18	2018	1	21	Online	0	0	0	77.00	1	Not_Canceled
28297	INN28298	1	0	2	5	Meal Plan 1	0	Room_Type 1	68	2018	8	29	Online	0	0	0	92.35	1	Not_Canceled
22021	INN22022	2	0	0	2	Meal Plan 1	0	Room_Type 1	42	2018	11	4	Offline	0	0	0	72.00	0	Not_Canceled
26586	INN26587	3	0	2	2	Meal Plan 1	0	Room_Type 4	53	2018	3	20	Online	0	0	0	124.10	1	Canceled
16756	INN16757	2	0	1	2	Meal Plan 1	0	Room_Type 1	305	2018	11	4	Offline	0	0	0	89.00	0	Canceled
20927	INN20928	2	0	2	6	Meal Plan 1	0	Room_Type 1	106	2018	7	8	Offline	0	0	0	72.25	2	Not_Canceled
35753	INN35754	2	0	1	2	Meal Plan 1	0	Room_Type 1	128	2018	6	20	Online	0	0	0	120.00	0	Canceled
9359	INN09360	1	0	1	0	Meal Plan 1	0	Room_Type 1	7	2018	5	16	Online	0	0	0	97.00	1	Not_Canceled
21929	INN21930	1	0	0	4	Meal Plan 1	0	Room_Type 4	48	2018	8	24	Online	0	0	0	149.40	1	Not_Canceled
17501	INN17502	2	0	0	3	Not Selected	0	Room_Type 1	27	2018	3	22	Online	0	0	0	129.00	0	Canceled
3355	INN03356	2	1	2	2	Meal Plan 1	0	Room_Type 1	173	2018	8	13	Online	0	0	0	114.75	1	Canceled
22183	INN22184	1	0	0	1	Meal Plan 1	0	Room_Type 1	9	2018	6	1	Online	0	0	0	97.02	1	Not_Canceled
7818	INN07819	2	0	0	1	Not Selected	0	Room_Type 1	57	2018	12	1	Online	0	0	0	79.20	2	Not_Canceled
26360	INN26361	2	0	0	2	Meal Plan 1	0	Room_Type 1	245	2018	6	17	Offline	0	0	0	75.00	0	Canceled
15193	INN15194	2	0	0	3	Meal Plan 2	0	Room_Type 1	36	2017	10	13	Offline	0	0	0	112.00	0	Not_Canceled
19873	INN19874	2	0	0	1	Not Selected	0	Room_Type 1	38	2018	7	2	Online	0	0	0	107.10	1	Not_Canceled
8015	INN08016	1	1	0	1	Meal Plan 2	0	Room_Type 1	29	2018	12	17	Online	0	0	0	130.00	0	Not_Canceled
36151	INN36152	2	0	2	2	Meal Plan 1	0	Room_Type 1	26	2018	2	7	Online	0	0	0	64.64	0	Not_Canceled
21254	INN21255	1	0	1	2	Meal Plan 1	0	Room_Type 1	6	2018	5	23	Online	0	0	0	97.02	1	Not_Canceled
16881	INN16882	2	0	1	0	Meal Plan 1	0	Room_Type 1	258	2018	10	16	Offline	0	0	0	110.00	0	Canceled
9465	INN09466	2	0	2	1	Meal Plan 1	0	Room_Type 4	118	2018	10	29	Online	0	0	0	104.40	1	Not_Canceled
18785	INN18786	1	0	0	2	Meal Plan 1	0	Room_Type 1	188	2018	6	15	Offline	0	0	0	130.00	0	Canceled
28097	INN28098	2	0	1	2	Meal Plan 1	0	Room_Type 1	39	2018	3	14	Offline	0	0	0	85.00	0	Not_Canceled
29239	INN29240	1	0	0	2	Meal Plan 1	0	Room_Type 1	32	2017	11	20	Offline	0	0	0	73.00	0	Not_Canceled
664	INN00665	2	1	1	1	Meal Plan 1	1	Room_Type 1	27	2018	8	8	Online	0	0	0	195.50	2	Not_Canceled
8285	INN08286	2	0	2	1	Not Selected	0	Room_Type 1	240	2018	12	10	Online	0	0	0	67.50	2	Canceled
4598	INN04599	2	0	1	0	Not Selected	0	Room_Type 1	127	2018	7	25	Online	0	0	0	94.50	1	Not_Canceled
21340	INN21341	2	2	0	3	Meal Plan 1	0	Room_Type 6	24	2018	4	14	Online	0	0	0	207.00	2	Not_Canceled
12098	INN12099	2	0	1	3	Meal Plan 1	0	Room_Type 1	262	2018	12	26	Online	0	0	0	73.95	0	Canceled
2904	INN02905	2	0	0	5	Meal Plan 1	0	Room_Type 4	41	2018	10	4	Online	0	0	0	139.50	2	Not_Canceled
21902	INN21903	2	0	2	3	Meal Plan 1	0	Room_Type 4	101	2018	5	5	Online	0	0	0	132.60	1	Not_Canceled

	0
Booking_ID	0
no_of_adults	0
no_of_children	0
no_of_weekend_nights	0
no_of_week_nights	0
type_of_meal_plan	0
required_car_parking_space	0
room_type_reserved	0
lead_time	0
arrival_year	0
arrival_month	0
arrival_date	0
market_segment_type	0
repeated_guest	0
no_of_previous_cancellations	0
no_of_previous_bookings_not_canceled	0
avg_price_per_room	0
no_of_special_requests	0
booking_status	0

	type_of_meal_plan	room_type_reserved	market_segment_type	booking_status
count	36275	36275	36275	36275
unique	4	7	5	2
top	Meal Plan 1	Room_Type 1	Online	Not_Canceled
freq	27835	28130	23214	24390

	no_of_adults	no_of_week_nights	lead_time	arrival_year	arrival_month	arrival_date	avg_price_per_room	booking_status	length_stay	no_of_children_log	no_of_weekend_nights_log	no_of_previous_cancellations_log	no_of_previous_bookings_not_canceled_log	no_of_special_requests_log	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	2	224	2017	10	2	65.00	False	3	0.693147	1.098612	0.693147	0.693147	0.693147	False	False	False	False	False	False	False	False	False	False	False	True	False
1	2	3	5	2018	11	6	106.68	False	5	0.693147	1.386294	0.693147	0.693147	1.098612	False	False	True	False	False	False	False	False	False	False	False	False	True
2	1	1	1	2018	2	28	60.00	True	3	0.693147	1.386294	0.693147	0.693147	0.693147	False	False	False	False	False	False	False	False	False	False	False	False	True
3	2	2	211	2018	5	20	100.00	True	2	0.693147	0.693147	0.693147	0.693147	0.693147	False	False	False	False	False	False	False	False	False	False	False	False	True
4	2	1	48	2018	4	11	94.50	True	2	0.693147	1.098612	0.693147	0.693147	0.693147	False	False	True	False	False	False	False	False	False	False	False	False	True

	lead_time
0	med
1	lat_min
2	lat_min
3	med
4	lat_min
5	long
6	lat_min
7	lat_min
8	short
9	lat_min

	ccp_alphas	impurities
0	0.000000	0.013864
1	0.000000	0.013864
2	0.000000	0.013864
3	0.000000	0.013864
4	0.000000	0.013864
...	...	...
1580	0.006666	0.286897
1581	0.013045	0.299942
1582	0.017260	0.317202
1583	0.023990	0.365183
1584	0.076578	0.441761

	Model	Train_Recall	Test_Recall
0	Initial decision tree model	0.981	0.792
1	Decision tree with restricted maximum depth	0.732	0.739
2	Decision treee with hyperparameter tuning	0.732	0.739
3	Decision tree with post-pruning	0.979	0.794

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-2.9998	0.339	-8.860	0.000	-3.663	-2.336
no_of_adults	0.2455	0.033	7.450	0.000	0.181	0.310
required_car_parking_space	-1.4839	0.129	-11.508	0.000	-1.737	-1.231
arrival_month	-0.0661	0.005	-12.288	0.000	-0.077	-0.056
repeated_guest	-2.8119	0.485	-5.798	0.000	-3.762	-1.861
avg_price_per_room	0.0228	0.001	36.083	0.000	0.022	0.024
length_stay	0.1162	0.009	13.454	0.000	0.099	0.133
no_of_children_log	0.8407	0.142	5.934	0.000	0.563	1.118
no_of_previous_cancellations_log	1.3364	0.450	2.971	0.003	0.455	2.218
no_of_special_requests_log	-3.2162	0.068	-47.407	0.000	-3.349	-3.083
type_of_meal_plan_Meal Plan 2	-0.3171	0.053	-5.990	0.000	-0.421	-0.213
type_of_meal_plan_Meal Plan 3	1.7139	2.880	0.595	0.552	-3.931	7.358
type_of_meal_plan_Not Selected	0.8080	0.045	17.965	0.000	0.720	0.896
room_type_reserved_Room_Type 4	0.0529	0.047	1.131	0.258	-0.039	0.145
room_type_reserved_Room_Type 5	-0.8715	0.180	-4.849	0.000	-1.224	-0.519
room_type_reserved_Room_Type 6	-1.1150	0.128	-8.713	0.000	-1.366	-0.864
room_type_reserved_Room_Type 7	-1.7747	0.277	-6.405	0.000	-2.318	-1.232
lead_time_y_short	1.3150	0.036	36.231	0.000	1.244	1.386
lead_time_y_med	2.8049	0.054	52.201	0.000	2.700	2.910
lead_time_y_long	3.0393	0.073	41.703	0.000	2.896	3.182
lead_time_y_advanced	4.5477	0.236	19.293	0.000	4.086	5.010

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-3.0179	0.338	-8.916	0.000	-3.681	-2.354
no_of_adults	0.2541	0.032	7.922	0.000	0.191	0.317
required_car_parking_space	-1.4849	0.129	-11.519	0.000	-1.738	-1.232
arrival_month	-0.0663	0.005	-12.334	0.000	-0.077	-0.056
repeated_guest	-2.8085	0.485	-5.789	0.000	-3.759	-1.858
avg_price_per_room	0.0230	0.001	38.361	0.000	0.022	0.024
length_stay	0.1173	0.009	13.669	0.000	0.100	0.134
no_of_children_log	0.8249	0.141	5.849	0.000	0.549	1.101
no_of_previous_cancellations_log	1.3352	0.450	2.966	0.003	0.453	2.217
no_of_special_requests_log	-3.2133	0.068	-47.410	0.000	-3.346	-3.080
type_of_meal_plan_Meal Plan 2	-0.3263	0.052	-6.236	0.000	-0.429	-0.224
type_of_meal_plan_Meal Plan 3	1.7231	2.937	0.587	0.557	-4.034	7.480
type_of_meal_plan_Not Selected	0.7971	0.044	18.154	0.000	0.711	0.883
room_type_reserved_Room_Type 5	-0.8887	0.179	-4.963	0.000	-1.240	-0.538
room_type_reserved_Room_Type 6	-1.1382	0.126	-9.011	0.000	-1.386	-0.891
room_type_reserved_Room_Type 7	-1.8140	0.275	-6.596	0.000	-2.353	-1.275
lead_time_y_short	1.3134	0.036	36.222	0.000	1.242	1.385
lead_time_y_med	2.8006	0.054	52.257	0.000	2.696	2.906
lead_time_y_long	3.0337	0.073	41.714	0.000	2.891	3.176
lead_time_y_advanced	4.5433	0.236	19.275	0.000	4.081	5.005